Influence of surface water activity on freezing/thawing times and weight loss prediction

2007 ◽  
Vol 83 (1) ◽  
pp. 23-30 ◽  
Author(s):  
A.E. Delgado ◽  
Da-Wen Sun
Keyword(s):  
Weight Loss ◽  
Surface Water ◽  
Water Activity ◽  
Loss Prediction

Impact of a novel spray-chilling system on surface microflora, water activity and weight loss during beef carcass chilling

2006 ◽  
Vol 23 (5) ◽  
pp. 483-490 ◽  
Author(s):  
K.J. Kinsella ◽  
J.J. Sheridan ◽  
T.A. Rowe ◽  
F. Butler ◽  
A. Delgado ◽  
...  
Keyword(s):  
Weight Loss ◽  
Water Activity ◽  
Beef Carcass

scholarly journals Microbiota indigenous milk, mesophilic lipolytic and proteolytic colonial cheese matured, produced at different times of the year

2017 ◽  
Vol 18 (4) ◽  
pp. 549-559
Author(s):  
Elisângela Borsoi Pereira ◽  
Magali Soares dos Santos Pozza ◽  
Paula Martins Olivo ◽  
Osmar Dalla Santa ◽  
Suzana da Cruz Pires ◽  
...  
Keyword(s):  
Weight Loss ◽  
Lactic Acid ◽  
Experimental Design ◽  
Relative Humidity ◽  
Water Activity ◽  
Cultural Importance ◽  
The Third ◽  
Randomized Experimental Design ◽  
Double Factorial ◽  
Production Period

SUMMARY Cheese is the oldest form of preserving milk nutrients having nutritional, economic and cultural importance. The objective of this study was to identify the best time of the year for production, and period, in months, for maturation of traditional colonial cheese, through analysis of water activity, weight loss and counts of lactic acid, mesophilic microorganisms—proteolytic and lipolytic. Records of temperature and relative humidity (RH) were maintained. A completely randomized experimental design was used in a double factorial scheme, considering production periods and maturation times. For all production periods evaluated, there was a significant reduction in the periods for water activity values. The counts of lactic acid bacteria ranged from 104 to 109 CFU/g. There was also stability in the number of colonies for lipolytic mesophilic microorganisms, until the third month of maturation. Low counts of proteolytic mesophiles were observed for the samples produced in May and June (5.70 and 5.53 log), respectively. The production period for the months of May and June corresponding to RH of 80% and average temperatures of 15°C were the most effective for production. Due to the presence of Listeria, it is recommended to respect the minimum time of 60 days of maturation for commercialization.

Weight Loss Prediction by Operating Conditions of CA Storage

2017 ◽  
Vol 21 (4) ◽  
pp. 312-317 ◽  
Author(s):  
Chun Wan Park ◽  
Seok Ho Park ◽  
Jin Se Kim ◽  
Dong Soo Choi ◽  
Yong Hun Kim ◽  
...  
Keyword(s):  
Weight Loss ◽  
Operating Conditions ◽  
Loss Prediction

scholarly journals Data integration for prediction of weight loss in randomized controlled dietary trials

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rikke Linnemann Nielsen ◽  
Marianne Helenius ◽  
Sara L. Garcia ◽  
Henrik M. Roager ◽  
Derya Aytan-Aktug ◽  
...  
Keyword(s):  
Weight Loss ◽  
Weight Management ◽  
Dietary Intervention ◽  
Bacterial Species ◽  
Management Strategies ◽  
Urine Metabolites ◽  
Individual Level ◽  
Loss Prediction ◽  
Random Forest Models ◽  
The Individual

AbstractDiet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84–0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies.

scholarly journals Utility of a Clinical Decision Support System in Weight Loss Prediction After Head and Neck Cancer Radiotherapy

2019 ◽  
pp. 1-11
Author(s):  
Zhi Cheng ◽  
Minoru Nakatsugawa ◽  
Xian Chong Zhou ◽  
Chen Hu ◽  
Stephen Greco ◽  
...  
Keyword(s):  
Weight Loss ◽  
Head And Neck Cancer ◽  
Decision Support ◽  
Prediction Model ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Loss Probability ◽  
Significant Weight Loss ◽  
Data Set ◽  
Loss Prediction

PURPOSE To evaluate the utility of a clinical decision support system (CDSS) using a weight loss prediction model. METHODS A prediction model for significant weight loss (loss of greater than or equal to 7.5% of body mass at 3-month post radiotherapy) was created with clinical, dosimetric, and radiomics predictors from 63 patients in an independent training data set (accuracy, 0.78; area under the curve [AUC], 0.81) using least absolute shrinkage and selection operator logistic regression. Four physicians with varying experience levels were then recruited to evaluate 100 patients in an independent validation data set of head and neck cancer twice (ie, a pre-post design): first without and then with the aid of a CDSS derived from the prediction model. At both evaluations, physicians were asked to predict the development (yes/no) and probability of significant weight loss for each patient on the basis of patient characteristics, including pretreatment dysphagia and weight loss and information from the treatment plan. At the second evaluation, physicians were also provided with the prediction model’s results for weight loss probability. Physicians’ predictions were compared with actual weight loss, and accuracy and AUC were investigated between the two evaluations. RESULTS The mean accuracy of the physicians’ ability to identify patients who will experience significant weight loss (yes/no) increased from 0.58 (range, 0.47 to 0.63) to 0.63 (range, 0.58 to 0.72) with the CDSS ( P = .06). The AUC of weight loss probability predicted by physicians significantly increased from 0.56 (range, 0.46 to 0.64) to 0.69 (range, 0.63 to 0.73) with the aid of the CDSS ( P < .05). Specifically, more improvement was observed among less-experienced physicians ( P < .01). CONCLUSION Our preliminary results demonstrate that physicians’ decisions may be improved by a weight loss CDSS model, especially among less-experienced physicians. Additional study with a larger cohort of patients and more participating physicians is thus warranted for understanding the usefulness of CDSSs.

scholarly journals Use of Decompression Instant Vacuum (DIC) Method into Vegetal Snack Production from Dehydrated Apple (cv. Reineta) and Pear (cv.Conferencia)

2013 ◽  
Vol 1 (2) ◽  
pp. 125-130 ◽  
Author(s):  
M De la Fuente ◽  
O Folgueral ◽  
B Prieto ◽  
J Fresno ◽  
M González-Raurich
Keyword(s):  
Weight Loss ◽  
Moisture Content ◽  
Water Activity ◽  
Industrial Applications ◽  
Raw Material ◽  
Fruit Species

One of the raising dehydration techniques more recently developed is instant decompression controlled vacuum (DIC; Détente instantannée Contrôlée), which causes a modification of micro- and macrostructure of food causing an expansion of the product, which improves their texture, color and rehydration capacity. The objective of this study was to evaluate specifically cultivars of pear (Conferencia) and apple (Reineta) from Bierzo (León, Spain) as raw material during processing from dehydrated process (DIC) and determines their suitability for snack industrial applications. To this end, samples of each fruit species were taken at harvest (0 weeks), and after 6 and 12 weeks of storage, peeled and sliced in two formats (2 and 4 mm). Several parameters were measured before (ºBrix, firmness and weight loss) and after (moisture content and water activity) DIC process. Results indicate that apple shows more stable for any format-studied compare to pear during DIC process, since it presented less variability (17%) than pear (46%) according to the applied format. The format modifies aw values registered during the entire DIC process; because 2mm slices have lower water activity and moisture content values than 4mm slices. Apple marks hardness parameters along its storage (<4.5 kg·cm-2), that provided greater firmness and consistency than pear along storage, so all studied parameters reflected that apple was more suitable for DIC process than pear.

scholarly journals The influence of roasting temperature on the physical properties of Arabica and Robusta coffee

2019 ◽  
pp. 172-178
Author(s):  
Bozana Odzakovic ◽  
Natalija Dzinic ◽  
Marija Jokanovic ◽  
Slavica Grujic
Keyword(s):  
Weight Loss ◽  
Physical Properties ◽  
Bulk Density ◽  
Water Activity ◽  
Total Weight ◽  
Total Weight Loss ◽  
Good Tool ◽  
Important Indicator ◽  
Robusta Coffee ◽  
Roasting Temperature

The influence of different roasting temperatures on the physical properties of Arabica and Robusta coffee was investigated. Arabica (Rio Minas) of two quality classes and Robusta coffee samples, roasted at 167, 171 and 175?C, were used for this research. Bulk density, total weight loss, water activity, texture and colour of coffee were determined. Total weight loss increased, and bulk density of coffee beans decreased with increasing of roasting temperature. Arabica 1st class with the highest weight and volume also had the highest total weight loss and Robusta had the highest bulk density. Coffee samples roasted at 175?C had significantly lower (P<0.05) water activity values than samples roasted at 167?C and those changes mostly affected mechanical properties of beans. The highest breaking force values had Arabica 1st class, with lower share of black beans, roasted at 167?C and Arabica 2nd class roasted at 171 and 175?C, compared to other samples roasted at the same temperatures. Robusta beans had the most brittle and fragile texture. It can be concluded that texture of coffee depends on the roasting temperature, type and class of coffee as well. L*, a*, and b* colour parameters decreased as the roasting temperature increased, which affected on coffee beverage colour defined as brown (167?C) and dark brown (171?C and 175?C) with different share of yellowish, orange, reddish and grey shades. Sensory analysis can be used as a good tool for a detailed description of coffee colour, as an important indicator of quality, especially in industrial conditions of coffee production.

Weight loss prediction during meat chilling

Meat Science ◽  
1980 ◽  
Vol 5 (1) ◽  
pp. 5-15 ◽  
Author(s):  
A.N. Califano ◽  
A. Calvelo
Keyword(s):  
Weight Loss ◽  
Loss Prediction
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